Interface

By Michael Hay, Vibhor Rastogi, Gerome Miklau, Dan Suciu. 2007

Mobilising Knowledge Resources in the Global South: Learning by Doing and Learning by Thinking Between Planning Practice and the Academic World: Some Comments from a Brazilian Experience Thinking about Johannesburg Learning by Doing Finding and Sharing Knowledge Resources in the Global South

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Badih Ghazi
2. Tianhao Wang
3. Ravi Kumar
4. Ninghui Li
5. Rasmus Pagh
6. Bolin Ding
7. Somesh Jha
8. Graham Cormode

Citations in the corpus

• Differentially Private Hierarchical Group Size Estimation (2018, methodology): “However, we show that some unattributed histogram algorithms [19, 24] can be adapted for this task.”
• Differentially Private Hierarchical Count-of-Counts Histograms (2018, methodology): “They are much more accurate than the naive strategy of adding noise to each group and selecting the k th largest noisy group [19].”
• Answering Multi-Dimensional Analytical Queries under Local Differential Privacy (2019, methodology): “We propose a mechanism (A HI , P HI ) , whose one-dimensional to ensure that the error is sublinear in m . have also been used by previous work, e.g. , [22, 28], to answer Hierarchy of intervals.”
• The Optimal Mechanism in Differential Privacy : Multidimensional Setting (2013, background): “[5] show that for a general class of histogram queries, by exploiting the consistency constraints on the query output, which is differentially private by adding independent Laplace noises, one can improve the accuracy while still satisfying differential privacy.”
• EKTELO: A Framework for Defining Differentially-Private Computations (2018, methodology): “The use of inference appears in many di↵erentially private algorithms [3, 5, 7, 18, 22, 23, 25, 31, 36, 37, 43].”
• Optimizing error of high-dimensional statistical queries under differential privacy (2018, unknown): “This is the same error metric proposed originally by the matrix mechanism, as well as a number of other works [19,24,28,34,41].”
• Non-tracking web analytics (2012, background): “Many original uses of differential privacy, however, assume the existence of a central database controlling the disclosure of results [28, 41].”
• Luminescent metal nanoclusters: Biosensing strategies and bioimaging applications (2021, methodology): “5-20 μM.[121] Moreover, by triggering the aggregation of GSH-Ag NCs by Al3+, an AIE-based pyrophosphatase (PPase) assay was developed (Figure 6C).”
• Real-time aggregate monitoring with differential privacy (2012, background): “Most existing works on differentially private data release deal with one-time release of static data [4, 7, 13, 14, 15, 16].”
• Private Heavy Hitters and Range Queries in the Shuffled Model (2019, methodology): “Xiao et al. [48] showed how to obtain differentially private range count queries by using Haar wavelets, while Hay et al. [34] formalized the method of maintaining a hierarchical representation of data; the aforementioned two works were compared and refined by Qardaji et al. [42].”
• Locally Differentially Private Heavy Hitter Identification (2017, methodology): “the frequent strings, a post-processing step modified from [18] is used tomake the final estimationmore accurate.”